New Centrality Measures in Networks

New Centrality Measures in Networks

Author: Fuad Aleskerov

Publisher: CRC Press

Published: 2021-12-07

Total Pages: 114

ISBN-13: 1000536106

DOWNLOAD EBOOK

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes, e.g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally, it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus, critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks, international migration, global trade, global food network, arms transfers, networks of terrorist groups, and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas, the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance, migration, trade, etc.) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github.com/SergSHV/slric.


Book Synopsis New Centrality Measures in Networks by : Fuad Aleskerov

Download or read book New Centrality Measures in Networks written by Fuad Aleskerov and published by CRC Press. This book was released on 2021-12-07 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes, e.g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally, it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus, critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks, international migration, global trade, global food network, arms transfers, networks of terrorist groups, and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas, the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance, migration, trade, etc.) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github.com/SergSHV/slric.

New Centrality Measures in Networks

New Centrality Measures in Networks

Author: Faud Tagi ogly Aleskerov

Publisher:

Published: 2021-12

Total Pages:

ISBN-13: 9781032066974

DOWNLOAD EBOOK

"Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes, e.g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally, it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus, critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields - financial networks, international migration, global trade, global food network, arms transfers, networks of terrorist groups, networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas, the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance, migration, trade, etc.) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https: //github.com/SergSHV/slric"--


Book Synopsis New Centrality Measures in Networks by : Faud Tagi ogly Aleskerov

Download or read book New Centrality Measures in Networks written by Faud Tagi ogly Aleskerov and published by . This book was released on 2021-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes, e.g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally, it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus, critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields - financial networks, international migration, global trade, global food network, arms transfers, networks of terrorist groups, networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas, the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance, migration, trade, etc.) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https: //github.com/SergSHV/slric"--

New Centrality Measures in Networks

New Centrality Measures in Networks

Author: FUAD. SHVYDUN ALESKEROV (SERGEY. MESHCHERYAKOVA, NATALIA.)

Publisher: CRC Press

Published: 2021-12-07

Total Pages: 102

ISBN-13: 9781032063195

DOWNLOAD EBOOK

This book presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields.


Book Synopsis New Centrality Measures in Networks by : FUAD. SHVYDUN ALESKEROV (SERGEY. MESHCHERYAKOVA, NATALIA.)

Download or read book New Centrality Measures in Networks written by FUAD. SHVYDUN ALESKEROV (SERGEY. MESHCHERYAKOVA, NATALIA.) and published by CRC Press. This book was released on 2021-12-07 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a class of new centrality measures which take into account individual attributes of nodes, the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields.

Complex Networks

Complex Networks

Author: Ronaldo Menezes

Publisher: Springer

Published: 2012-07-27

Total Pages: 265

ISBN-13: 3642302874

DOWNLOAD EBOOK

In the last decade we have seen the emergence of a new inter-disciplinary field concentrating on the understanding large networks which are dynamic, large, open, and have a structure that borders order and randomness. The field of Complex Networks has helped us better understand many complex phenomena such as spread of decease, protein interaction, social relationships, to name but a few. The field of Complex Networks has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modeled by random graphs. But modern network research could show that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). The current theories coupled with the availability of data makes the field of Complex Networks (sometimes called Network Sciences) one of the most promising interdisciplinary disciplines of today. This sample of works in this book gives as a taste of what is in the horizon such controlling the dynamics of a network and in the network, using social interactions to improve urban planning, ranking in music, and the understanding knowledge transfer in influence networks.


Book Synopsis Complex Networks by : Ronaldo Menezes

Download or read book Complex Networks written by Ronaldo Menezes and published by Springer. This book was released on 2012-07-27 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade we have seen the emergence of a new inter-disciplinary field concentrating on the understanding large networks which are dynamic, large, open, and have a structure that borders order and randomness. The field of Complex Networks has helped us better understand many complex phenomena such as spread of decease, protein interaction, social relationships, to name but a few. The field of Complex Networks has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modeled by random graphs. But modern network research could show that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). The current theories coupled with the availability of data makes the field of Complex Networks (sometimes called Network Sciences) one of the most promising interdisciplinary disciplines of today. This sample of works in this book gives as a taste of what is in the horizon such controlling the dynamics of a network and in the network, using social interactions to improve urban planning, ranking in music, and the understanding knowledge transfer in influence networks.

Network Analysis

Network Analysis

Author: Ulrik Brandes

Publisher: Springer

Published: 2005-02-02

Total Pages: 481

ISBN-13: 3540319557

DOWNLOAD EBOOK

‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.


Book Synopsis Network Analysis by : Ulrik Brandes

Download or read book Network Analysis written by Ulrik Brandes and published by Springer. This book was released on 2005-02-02 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: ‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.

Centrality Metrics for Complex Network Analysis

Centrality Metrics for Complex Network Analysis

Author: Natarajan Meghanathan

Publisher: Information Science Reference

Published: 2018

Total Pages: 0

ISBN-13: 9781522538028

DOWNLOAD EBOOK

"This book explores node and edge centrality metrics and real-world network graphs, computationally-light vs. computationally-heavy centrality metrics, centrality-based connected dominating sets for complex network graphs, assortativity analysis based on centrality metrics, time-dependent variation of the node centrality metrics during the evolution of a scale-free network, curriculum network graph analysis, and eigenvector centrality-based approach to detect graph isomorphism"--


Book Synopsis Centrality Metrics for Complex Network Analysis by : Natarajan Meghanathan

Download or read book Centrality Metrics for Complex Network Analysis written by Natarajan Meghanathan and published by Information Science Reference. This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book explores node and edge centrality metrics and real-world network graphs, computationally-light vs. computationally-heavy centrality metrics, centrality-based connected dominating sets for complex network graphs, assortativity analysis based on centrality metrics, time-dependent variation of the node centrality metrics during the evolution of a scale-free network, curriculum network graph analysis, and eigenvector centrality-based approach to detect graph isomorphism"--

Frontiers in Algorithmics

Frontiers in Algorithmics

Author: Franco P. Preparata

Publisher: Springer Science & Business Media

Published: 2008-05-30

Total Pages: 360

ISBN-13: 3540693106

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the Second International Frontiers of Algorithmics Workshop, FAW 2008, held in Changsha, China, in June 2008. The 33 revised full papers presented together with the abstracts of 3 invited talks were carefully reviewed and selected from 80 submissions. The papers were selected for 9 special focus tracks in the areas of biomedical informatics, discrete structures, geometric information processing and communication, games and incentive analysis, graph algorithms, internet algorithms and protocols, parameterized algorithms, design and analysis of heuristics, approximate and online algorithms, and machine learning.


Book Synopsis Frontiers in Algorithmics by : Franco P. Preparata

Download or read book Frontiers in Algorithmics written by Franco P. Preparata and published by Springer Science & Business Media. This book was released on 2008-05-30 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Frontiers of Algorithmics Workshop, FAW 2008, held in Changsha, China, in June 2008. The 33 revised full papers presented together with the abstracts of 3 invited talks were carefully reviewed and selected from 80 submissions. The papers were selected for 9 special focus tracks in the areas of biomedical informatics, discrete structures, geometric information processing and communication, games and incentive analysis, graph algorithms, internet algorithms and protocols, parameterized algorithms, design and analysis of heuristics, approximate and online algorithms, and machine learning.

STACS 2005

STACS 2005

Author: Volker Diekert

Publisher: Springer

Published: 2005-02-02

Total Pages: 722

ISBN-13: 3540318569

DOWNLOAD EBOOK

This book constitutes the refereed proceedings of the 22nd Annual Symposium on Theoretical Aspects of Computer Science, STACS 2005, held in Stuttgart, Germany in February 2005. The 54 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 217 submissions. A broad variety of topics from theoretical computer science are addressed, in particular complexity theory, algorithmics, computational discrete mathematics, automata theory, combinatorial optimization and approximation, networking and graph theory, computational geometry, grammar systems and formal languages, etc.


Book Synopsis STACS 2005 by : Volker Diekert

Download or read book STACS 2005 written by Volker Diekert and published by Springer. This book was released on 2005-02-02 with total page 722 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd Annual Symposium on Theoretical Aspects of Computer Science, STACS 2005, held in Stuttgart, Germany in February 2005. The 54 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 217 submissions. A broad variety of topics from theoretical computer science are addressed, in particular complexity theory, algorithmics, computational discrete mathematics, automata theory, combinatorial optimization and approximation, networking and graph theory, computational geometry, grammar systems and formal languages, etc.

Path Centrality

Path Centrality

Author: Tharaka Alahakoon

Publisher:

Published: 2010

Total Pages:

ISBN-13:

DOWNLOAD EBOOK

ABSTRACT: In network analysis, it is useful to identify important vertices in a network. Based on the varying notions of importance of vertices, a number of centrality measures are defined and studied in the literature. Some popular centrality measures, such as betweenness centrality, are computationally prohibitive for large-scale networks. In this thesis, we propose a new centrality measure called k-path centrality and experimentally compare this measure with betweenness centrality. We present a polynomial-time randomized algorithm for distinguishing high k-path centrality vertices from low k-path centrality vertices in any given (unweighted or weighted) graph. Specifically, for any graph G = (V, E) with n vertices and for every choice of parameters alpha between (0,1), epsilon between (0,1/2), and integer k between [1,n], with probability at least 1-1/n^2 our randomized algorithm distinguishes all vertices v in V that have k-path centrality Ck(v) more than n^(alpha)*(1+2*epsilon) from all vertices v in V that have k-path centrality Ck(v) less than n^(alpha)*(1-2*epsilon). The running time of the algorithm is O(k^(2)*epsilon^( -2)*n^(1-alpha)*ln(n)). Next, we present a polynomial-time randomized approximation algorithm for computing the k-path centrality values of all vertices in any given (unweighted or weighted) graph. Specifically, for any graph and for every choice of parameters alpha between (0,1/2) and integer k between [1,n], with probability at least 1-1/n^2 our randomized approximation algorithm computes the k-path centrality value of every vertex within an additive error of at most n^(1/2+alpha). The running time of the algorithm is O(k^(3)*n^(1-2*alpha)*ln(n)). Theoretically and experimentally, our algorithms are (for suitable choices of parameters) significantly faster than the best known deterministic algorithm for computing exact betweenness centrality values (Brandes' algorithm). Through experimentations on both real and randomly generated networks, we demonstrate that vertices that have high betweenness centrality values also have high k-path centrality values.


Book Synopsis Path Centrality by : Tharaka Alahakoon

Download or read book Path Centrality written by Tharaka Alahakoon and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: In network analysis, it is useful to identify important vertices in a network. Based on the varying notions of importance of vertices, a number of centrality measures are defined and studied in the literature. Some popular centrality measures, such as betweenness centrality, are computationally prohibitive for large-scale networks. In this thesis, we propose a new centrality measure called k-path centrality and experimentally compare this measure with betweenness centrality. We present a polynomial-time randomized algorithm for distinguishing high k-path centrality vertices from low k-path centrality vertices in any given (unweighted or weighted) graph. Specifically, for any graph G = (V, E) with n vertices and for every choice of parameters alpha between (0,1), epsilon between (0,1/2), and integer k between [1,n], with probability at least 1-1/n^2 our randomized algorithm distinguishes all vertices v in V that have k-path centrality Ck(v) more than n^(alpha)*(1+2*epsilon) from all vertices v in V that have k-path centrality Ck(v) less than n^(alpha)*(1-2*epsilon). The running time of the algorithm is O(k^(2)*epsilon^( -2)*n^(1-alpha)*ln(n)). Next, we present a polynomial-time randomized approximation algorithm for computing the k-path centrality values of all vertices in any given (unweighted or weighted) graph. Specifically, for any graph and for every choice of parameters alpha between (0,1/2) and integer k between [1,n], with probability at least 1-1/n^2 our randomized approximation algorithm computes the k-path centrality value of every vertex within an additive error of at most n^(1/2+alpha). The running time of the algorithm is O(k^(3)*n^(1-2*alpha)*ln(n)). Theoretically and experimentally, our algorithms are (for suitable choices of parameters) significantly faster than the best known deterministic algorithm for computing exact betweenness centrality values (Brandes' algorithm). Through experimentations on both real and randomly generated networks, we demonstrate that vertices that have high betweenness centrality values also have high k-path centrality values.

Computational Discrete Mathematics

Computational Discrete Mathematics

Author: Sriram Pemmaraju

Publisher: Cambridge University Press

Published: 2009-10-15

Total Pages: 615

ISBN-13: 1107268710

DOWNLOAD EBOOK

This book was first published in 2003. Combinatorica, an extension to the popular computer algebra system Mathematica®, is the most comprehensive software available for teaching and research applications of discrete mathematics, particularly combinatorics and graph theory. This book is the definitive reference/user's guide to Combinatorica, with examples of all 450 Combinatorica functions in action, along with the associated mathematical and algorithmic theory. The authors cover classical and advanced topics on the most important combinatorial objects: permutations, subsets, partitions, and Young tableaux, as well as all important areas of graph theory: graph construction operations, invariants, embeddings, and algorithmic graph theory. In addition to being a research tool, Combinatorica makes discrete mathematics accessible in new and exciting ways to a wide variety of people, by encouraging computational experimentation and visualization. The book contains no formal proofs, but enough discussion to understand and appreciate all the algorithms and theorems it contains.


Book Synopsis Computational Discrete Mathematics by : Sriram Pemmaraju

Download or read book Computational Discrete Mathematics written by Sriram Pemmaraju and published by Cambridge University Press. This book was released on 2009-10-15 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was first published in 2003. Combinatorica, an extension to the popular computer algebra system Mathematica®, is the most comprehensive software available for teaching and research applications of discrete mathematics, particularly combinatorics and graph theory. This book is the definitive reference/user's guide to Combinatorica, with examples of all 450 Combinatorica functions in action, along with the associated mathematical and algorithmic theory. The authors cover classical and advanced topics on the most important combinatorial objects: permutations, subsets, partitions, and Young tableaux, as well as all important areas of graph theory: graph construction operations, invariants, embeddings, and algorithmic graph theory. In addition to being a research tool, Combinatorica makes discrete mathematics accessible in new and exciting ways to a wide variety of people, by encouraging computational experimentation and visualization. The book contains no formal proofs, but enough discussion to understand and appreciate all the algorithms and theorems it contains.